Tuesday, 4 October 2016

ClinicSpeak & ResearchSpeak: halfway there to brain atrophy measurements becoming part of routine clinical practice

Are you ready for having your brain volume measured annually? #ClinicSpeak #MSBlog #ResearchSpeak

"At present the various automated brain volume measurement tools are not good enough to use on individual subjects in clinical practice. There are two issues; (1) physiological variability and (2) measurement error and variability. The physiological variability can be overcome by standardising hydration status, co-medication, alcohol status, time of day and other variables that are known to affect brain volume. The measurement variability and error is a technological challenge and should improve with time. At the moment the best we can do is select a cut-off in brain volume change above which we can be confident brain atrophy is clearly abnormal, for example above 0.6%, 0.8% or 1% per annum. This threshold will depend on the performance characteristics of the measurement tool. The other option is to switch to better, more reliable, neurodegenerative marker, for example CSF neurofilament levels. We are hedging our bets and doing both. We are test driving the MSmetrix software below and we are implementing regular CSF neurofilament monitoring in a select group of patients in whom we think the data will inform clinical decision making."

"The study below compares a commercial product (MSMetrix) with SIENA, an open-source, academic product. It looks as if the commercial product MSMetrix has the slight edge. These data will need to confirmed and compared to other techniques out there."

"Please watch this space; with a focus on reducing, or trying to prevent, end-organ damage in MS these tools are going to become increasingly important as part of routine clinical practice."

Smeets et al. Reliable measurements of brain atrophy in individual patients with multiple sclerosis. Brain Behav. 2016 Jul 19;6(9):e00518. eCollection 2016.

INTRODUCTION: As neurodegeneration is recognized as a major contributor to disability in multiple sclerosis (MS), brain atrophy quantification could have a high added value in clinical practice to assess treatment efficacy and disease progression, provided that it has a sufficiently low measurement error to draw meaningful conclusions for an individual patient.

METHOD: In this paper, we present an automated longitudinal method based on Jacobian integration for measuring whole-brain and gray matter atrophy based on anatomical magnetic resonance images (MRI), named MSmetrix. MSmetrix is specifically designed to measure atrophy in patients with MS, by including iterative lesion segmentation and lesion filling based on FLAIR and T1-weighted MRI scans.

RESULTS: MS metrix is compared with SIENA with respect to test-retest error and consistency, resulting in an average test-retest error on an MS data set of 0.13% (MS metrix) and 0.17% (SIENA) and a consistency error of 0.07% (MS metrix) and 0.05% (SIENA). On a healthy subject data set including physiological variability the test-retest is 0.19% (MS metrix) and 0.31% (SIENA).

CONCLUSION: Therefore, we can conclude that MSmetrix could be of added value in clinical practice for the follow-up of treatment and disease progression in MS patients.

CoI: we are part of a pilot study, funded by Novartis, that is exploring the feasibility of using MSmetrix in routine clinical practice.


  1. Where are we at implementing OCT in clinical practice? https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4703093/pdf/nihms737171.pdf

    1. If you are at Edinburgh you get an OCT, because they have one in the MS Unit.

      However please be aware that OCT is only a surrogate and detects stuff in the visual pathway from the visual cortex to the eye. This may or may not correlate with whats going on in the rest of the CNS, if MS is highly active it may also hit the visual pathway, however as to prediction there is so much in MS about prediction but at the individual level it does not say what will happen.

      I have seen so many talks where this or that parameter predicts outcome down the line and when I look at the regression line to show the correlation it is essentially flat and tell us very little, so bewared there are lots of misleading correlations based on the titles of the publications


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